[GitHub] [incubator-mxnet] reminisce commented on a change in pull request #14831: [numpy] Numpy dot

[GitBox <gi...@apache.org>]

reminisce commented on a change in pull request #14831: [numpy] Numpy dot
URL: https://github.com/apache/incubator-mxnet/pull/14831#discussion_r281449349
 
 

 ##########
 File path: src/operator/numpy/np_dot-inl.h
 ##########
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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file np_dot-inl.h
+ * \brief Function definition of matrix numpy-compatible dot operator
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_DOT_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_DOT_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "../tensor/dot-inl.h"
+#include "../tensor/elemwise_binary_op.h"
+#include "../tensor/broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+inline bool NumpyDotShape(const nnvm::NodeAttrs& attrs,
+                          mxnet::ShapeVector *in_attrs,
+                          mxnet::ShapeVector *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 2U);
+  CHECK_EQ(out_attrs->size(), 1U);
+
+  const mxnet::TShape& a_shape = in_attrs->at(0);
+  const mxnet::TShape& b_shape = in_attrs->at(1);
+
+  if (a_shape.ndim() == 1 && b_shape.ndim() == 1) {
+    // Case 1: both 1-D arrays, inner product of vectors
+    CHECK_EQ(a_shape[0], b_shape[0]);
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, mxnet::TShape(0, 0));
+  } else if (a_shape.ndim() == 2 && b_shape.ndim() == 2) {
+    // Case 2: both 2-D arrays, matrix multiplication
+    CHECK_EQ(a_shape[1], b_shape[0]);
+    mxnet::TShape mm_shape(2, 0);
+    mm_shape[0] = a_shape[0];
+    mm_shape[1] = b_shape[1];
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, mm_shape);
+  } else if (a_shape.ndim() == 0 && b_shape.ndim() == 0) {
+    // Case 3: both 0-D scalars, equivalent to multiply
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, mxnet::TShape(0, 0));
+  } else if (a_shape.ndim() == 0 || b_shape.ndim() == 0) {
+    // Case 3.5: either of them is a scalar, just scale by one of them
+    mxnet::TShape oshape = (a_shape.ndim() == 0) ? b_shape : a_shape;
 
 Review comment:
   I think this case actually covers `Case 3`?

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